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        "\n# Demo of OPTICS clustering algorithm\n\nFinds core samples of high density and expands clusters from them.\nThis example uses data that is generated so that the clusters have\ndifferent densities.\nThe :class:`sklearn.cluster.OPTICS` is first used with its Xi cluster detection\nmethod, and then setting specific thresholds on the reachability, which\ncorresponds to :class:`sklearn.cluster.DBSCAN`. We can see that the different\nclusters of OPTICS's Xi method can be recovered with different choices of\nthresholds in DBSCAN.\n\n"
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        "# Authors: Shane Grigsby <refuge@rocktalus.com>\n#          Adrin Jalali <adrin.jalali@gmail.com>\n# License: BSD 3 clause\n\n\nfrom sklearn.cluster import OPTICS, cluster_optics_dbscan\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Generate sample data\n\nnp.random.seed(0)\nn_points_per_cluster = 250\n\nC1 = [-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2)\nC2 = [4, -1] + .1 * np.random.randn(n_points_per_cluster, 2)\nC3 = [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2)\nC4 = [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2)\nC5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)\nC6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)\nX = np.vstack((C1, C2, C3, C4, C5, C6))\n\nclust = OPTICS(min_samples=50, xi=.05, min_cluster_size=.05)\n\n# Run the fit\nclust.fit(X)\n\nlabels_050 = cluster_optics_dbscan(reachability=clust.reachability_,\n                                   core_distances=clust.core_distances_,\n                                   ordering=clust.ordering_, eps=0.5)\nlabels_200 = cluster_optics_dbscan(reachability=clust.reachability_,\n                                   core_distances=clust.core_distances_,\n                                   ordering=clust.ordering_, eps=2)\n\nspace = np.arange(len(X))\nreachability = clust.reachability_[clust.ordering_]\nlabels = clust.labels_[clust.ordering_]\n\nplt.figure(figsize=(10, 7))\nG = gridspec.GridSpec(2, 3)\nax1 = plt.subplot(G[0, :])\nax2 = plt.subplot(G[1, 0])\nax3 = plt.subplot(G[1, 1])\nax4 = plt.subplot(G[1, 2])\n\n# Reachability plot\ncolors = ['g.', 'r.', 'b.', 'y.', 'c.']\nfor klass, color in zip(range(0, 5), colors):\n    Xk = space[labels == klass]\n    Rk = reachability[labels == klass]\n    ax1.plot(Xk, Rk, color, alpha=0.3)\nax1.plot(space[labels == -1], reachability[labels == -1], 'k.', alpha=0.3)\nax1.plot(space, np.full_like(space, 2., dtype=float), 'k-', alpha=0.5)\nax1.plot(space, np.full_like(space, 0.5, dtype=float), 'k-.', alpha=0.5)\nax1.set_ylabel('Reachability (epsilon distance)')\nax1.set_title('Reachability Plot')\n\n# OPTICS\ncolors = ['g.', 'r.', 'b.', 'y.', 'c.']\nfor klass, color in zip(range(0, 5), colors):\n    Xk = X[clust.labels_ == klass]\n    ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], 'k+', alpha=0.1)\nax2.set_title('Automatic Clustering\\nOPTICS')\n\n# DBSCAN at 0.5\ncolors = ['g', 'greenyellow', 'olive', 'r', 'b', 'c']\nfor klass, color in zip(range(0, 6), colors):\n    Xk = X[labels_050 == klass]\n    ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3, marker='.')\nax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], 'k+', alpha=0.1)\nax3.set_title('Clustering at 0.5 epsilon cut\\nDBSCAN')\n\n# DBSCAN at 2.\ncolors = ['g.', 'm.', 'y.', 'c.']\nfor klass, color in zip(range(0, 4), colors):\n    Xk = X[labels_200 == klass]\n    ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)\nax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], 'k+', alpha=0.1)\nax4.set_title('Clustering at 2.0 epsilon cut\\nDBSCAN')\n\nplt.tight_layout()\nplt.show()"
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